Over decades, liver cancer is a rising cause of death in Taiwan, and more and more researchers
are concerned about detecting hepatic tumors in computed tomography (CT) images. For clinical
applications in terms of diagnosis and treatment planning, image segmentation on abdominal CT is
indispensable. Patients with a large number of CT images need specialist physicians to identify, and
detecting tumor location correctly from many CT images has been a major challenge subsequently.
Therefore, this paper proposed a novel computer-aided detection (CAD) method that had high
classification accuracy for identifying tumors. The proposed method used a region growing algorithm
to segment liver CT images, employed REDUCT sets to reduce attributes, and then utilized a rough set
algorithm to enhance classification performance. To evaluate the classification performances, the
proposed method was compared with five different classification methods: decision tree (C4.5 and
REP (reduced error pruning)), multilayer perceptron, Naïve Bayes, and support vector machine
(SVM). The results indicate that the proposed method is superior to the listing methods in terms of
classification accuracy.
關聯:
Journal of Applied Science and Engineering 19(1), pp.65-74
